UAV-Served Energy Harvesting-Enabled M2M Networks for Green Industry-A Perspective of Energy Efficient Resource Management Scheme

As one of the most important metrics to sustainably provide communication services in green Industrial Internet of Things (IIoT), the problem of improving energy efficiency has constantly attracted extraordinary concerns from industry so far as to academia. In this paper, we intend to investigate the energy efficiency issue for an Energy Harvesting (EH)-enabled Machine-to-Machine (EH-M2M) communication underlaying Unmanned Aerial Vehicles (UAVs) networks from the perspective of resource management. Specifically, we are aiming at maximizing the average energy efficiency of EH-M2M communications by conjointly considering the EH time slot assignment, transmit power control and bandwidth allocation under the limitations of Quality of Service (QoS) and the available energy status of the EH-M2M devices. However, as the optimization problem is non-convex and NP-hard which is hard to tackle directly, we first transform the primitive objective function into a convex form equivalently by non-linear fractional programming and variable relaxation approach. After that, an iterative algorithm on the basis of Dinkelbach and Lagrangian theory is designed to optimize the resource management strategy. Finally, extensive simulation results demonstrate that the proposed scheme can establish more energy efficient communications compared to the benchmark schemes in different network settings.